Evidence map›Paper›PMID 37137338›Full record

ArticleApplied clinical informatics2023

Using Existing Clinical Information Models for Dutch Quality Registries to Reuse Data and Follow COUMT Paradigm.

Maike H J Schepens, Annemarie C Trompert, Miranda L van Hooff, Erik van der Velde, Marjon Kallewaard, Iris J A M Verberk-Jonkers, Huib A Cense, Diederik M Somford, Sjoerd Repping, Selma C Tromp and 1 more

Abstract read
In one paragraph

Article in Applied clinical informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Maike H J SchepensCirka BV, Healthcare Strategy and Innovation, Zeist, The Netherlands.
Annemarie C TrompertDutch Institute for Clinical Auditing, Leiden, The Netherlands.
Miranda L van HooffDepartment of Orthopedics, Radboud UMC, Nijmegen, The Netherlands.
Erik van der VeldeDutch Association of Medical Specialists, Utrecht, The Netherlands.
Marjon KallewaardDutch Association of Medical Specialists, Utrecht, The Netherlands.
Iris J A M Verberk-JonkersDutch Association of Medical Specialists, Utrecht, The Netherlands.
Huib A CenseDepartment of Surgery, Rode Kruis Hospital, Beverwijk, The Netherlands.
Diederik M SomfordDepartment of Urology, Canisius Wilhelmina Hospital, Nijmegen, The Netherlands.
Sjoerd ReppingAmsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
Selma C TrompDutch Association of Medical Specialists, Utrecht, The Netherlands.
Michel W J M WoutersDepartment of Biomedical Data Sciences, LUMC, Leiden, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundReuse of health care data for various purposes, such as the care process, for quality measurement, research, and finance, will become increasingly important in the future; therefore, "Collect Once Use Many Times" (COUMT). Clinical information models (CIMs) can be used for content standardization. Data collection for national quality registries (NQRs) often requires manual data entry or batch processing. Preferably, NQRs collect required data by extracting data recorded during the health care process and stored in the electronic health record.

objectivesThe first objective of this study was to analyze the level of coverage of data elements in NQRs with developed Dutch CIMs (DCIMs). The second objective was to analyze the most predominant DCIMs, both in terms of the coverage of data elements as well as in their prevalence across existing NQRs.

methodsFor the first objective, a mapping method was used which consisted of six steps, ranging from a description of the clinical pathway to a detailed mapping of data elements. For the second objective, the total number of data elements that matched with a specific DCIM was counted and divided by the total number of evaluated data elements.

resultsAn average of 83.0% (standard deviation: 11.8%) of data elements in studied NQRs could be mapped to existing DCIMs . In total, 5 out of 100 DCIMs were needed to map 48.6% of the data elements.

conclusionThis study substantiates the potential of using existing DCIMs for data collection in Dutch NQRs and gives direction to further implementation of DCIMs. The developed method is applicable to other domains. For NQRs, implementation should start with the five DCIMs that are most prevalently used in the NQRs. Furthermore, a national agreement on the leading principle of COUMT for the use and implementation for DCIMs and (inter)national code lists is needed.

Indexed as

Delivery of Health CareElectronic Health RecordsRegistries

Identifiers

PMID37137338
PMCPMC10156444

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.